• Journal of Biosystems Engineering
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• v.42 no.4
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• pp.330-338
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• 2017

Purpose: The smartphone is actively being used in many research fields, primarily in medical and diagnostic applications. However, there are cases in which smartphone-based systems have been developed for agriculture, environment, and food applications. The purpose of this review is to summarize the research cases using smartphone cameras in agriculture, environment, and food. Methods: This review introduces seventeen research cases which used smartphone cameras in agriculture, food, water, and soil applications. These were classified as systems involving "smartphone-camera-alone" and "smartphone camera with optical accessories". Results: Detecting food-borne pathogens, analyzing the quality of foods, monitoring water quality and safety, gathering information regarding plant growth or damage, identifying weeds, and measuring soil loss after rain were presented for the smartphone-camera-alone system. Measuring food and water quality and safety, phenotyping seeds, and soil classifications were presented for the smartphone camera with optical accessories. Conclusions: Smartphone cameras were applied in various areas for several purposes. The use of smartphone cameras has advantages regarding high-resolution imaging, manual or auto exposure and focus control, ease of use, portability, image storage, and most importantly, programmability. The studies discussed were achieved by sensitivity improvements of CCDs (charge-coupled devices) and CMOS (complementary metal-oxide-semiconductor) on smartphone cameras and improved computing power of the smartphone, respectively. A smartphone camera-based system can be used with ease, low cost, in near-real-time, and on-site. This review article presents the applications and potential of the smartphone and the smartphone camera used for various purposes in agriculture, environment, and food.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.8-15
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• 2018

worker-centered safety management for hazardous areas in the plant is required. The causes of gas accidents in the past five years are closely related to the behavior of the operator, such as careless handling of the user, careless handling of the suppliers, and intentional, as well as equipment failure and accident of thought. In order to prevent such accidents, real-time monitoring of hazardous areas in the plant is required. However, when installing a camera in a work space for real-time monitoring, problems such as human rights abuse occur. In order to prevent this, an infrared camera with low resolution with low exposure of the operator is used. In real-time monitoring, image analysis is performed using CNN algorithm, not human, to prevent human rights violation.

• ETRI Journal
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• v.34 no.1
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• pp.138-141
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• 2012

This letter proposes a real-time digital image stabilization system for cell phone cameras without the need for frame memory. The system post-processes an image captured with a safe shutter speed using an adaptive denoising filter and a global color correction algorithm. This system can transfer the normal brightness of an image previewed under long exposure to the captured image making it bright and crisp with low noise. It is even possible to take photos in low-light conditions. By not needing frame memory, the approach is feasible for integration into the size-constrained image sensors of cell phone cameras.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.282-294
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• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• Research in Vestibular Science
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• v.17 no.4
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• pp.134-141
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• 2018

Objectives: We aimed to study the role of vestibular input on spatial memory performance in mice that had undergone bilateral surgical labyrinthectomy, semicircular canal (SCC) occlusion and 4G hypergravity exposure. Methods: Twelve to 16 weeks old ICR mice (n=30) were used for the experiment. The experimental group divided into 3 groups. One group had undergone bilateral chemical labyrinthectomy, and the other group had performed SCC occlusion surgery, and the last group was exposed to 4G hypergravity for 2 weeks. The movement of mice was recorded using camera in Y maze which had 3 radial arms (35 cm long, 7 cm high, 10 cm wide). We counted the number of visiting arms and analyzed the information of arm selection using program we developed before and after procedure. Results: The bilateral labyrinthectomy group which semicircular canal and otolithic function was impaired showed low behavioral performance and spacial memory. The semicircular canal occlusion with $CO_2$ laser group which only semicircular canal function was impaired showed no difference in performance activity and spatial memory. However the hypergravity exposure group in which only otolithic function impaired showed spatial memory function was affected but the behavioral performance was spared. The impairment of spatial memory recovered after a few days after exposure in hypergravity group. Conclusions: This spatial memory function was affected by bilateral vestibular loss. Space-related information processing seems to be determined by otolithic organ information rather than semicircular canals. Due to otolithic function impairment, spatial learning was impaired after exposure to gravity changes in animals and this impaired performance was compensated after normal gravity exposure.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.74-79
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• 2011

HDR(high dynamic range) imaging techniques supports wider dynamic range than normal images captured from general still camera. These usually need several shots to obtain LDR(low dynamic range) images, causing ghosting artifacts. Accordingly, this paper suggests a method to generate new LDR images from a single Bayer image using Exposure LUT(look-up table) by considering channel dependency. We prior construct exposure LUT for each RGB channel, showing the relationship between input and average output luminance values. In the process, by applying the average luminance of input image and current exposure to LUT, new exposures which are determined by user choice are first estimated. Next, LDR images which are corresponded to new exposures are generated based on each LUT. Saturated areas are improved by considering channel dependency in the last procedure. In the experimental comparison, high PSNR values are obtained between estimated and captured images. Also, we have similar appearance on displayed images.

• Journal of Ocean Engineering and Technology
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• v.26 no.5
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• pp.25-30
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• 2012

This study examined the use of LIT (lock-in infrared thermography) to detect defects in the welded parts of ships and offshore structures. A quantitative analysis was used with the filtering and texture measurement of image processing techniques to find the optimized experimental condition. We verified the reliability of our methods by applying image processing techniques in order to normalize the evaluations of comparative images that showed a phase difference. In addition, it was found that a low to mid-range intensity of light exposure on the surface showed good results, whereas high exposure did not provide significant results. A lock-in frequency of around 0.1 Hz was satisfactory regardless of the intensity of the light source. In addition, making the integration time of the thermography camera inversely proportional to the intensity of the exposed light source during the experiment provided good results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.587-598
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• 2021

This study aims to develop a multi-camera based MMS (Mobile Mapping System) technology for building a HD (High Definition) map for autonomous driving and for quick update. To replace expensive lidar sensors and reduce long processing times, we intend to develop a low-cost and efficient MMS by applying multiple cameras and real-time data pre-processing. To this end, multi-camera storage technology development, multi-camera time synchronization technology development, and MMS prototype development were performed. We developed a storage module for real-time JPG compression of high-speed images acquired from multiple cameras, and developed an event signal and GNSS (Global Navigation Satellite System) time server-based synchronization method to record the exposure time multiple images taken in real time. And based on the requirements of each sector, MMS was designed and prototypes were produced. Finally, to verify the performance of the manufactured multi-camera-based MMS, data were acquired from an actual 1,000 km road and quantitative evaluation was performed. As a result of the evaluation, the time synchronization performance was less than 1/1000 second, and the position accuracy of the point cloud obtained through SFM (Structure from Motion) image processing was around 5 cm. Through the evaluation results, it was found that the multi-camera based MMS technology developed in this study showed the performance that satisfies the criteria for building a HD map.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.224-227
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• 1999

This paper presents a design of the real-time preprocessor for CMOS image sensor suitable to the digital camera applications. CMOS image sensor offers some advantages in on-chip integration, system power reduction, and low cost. However, it has a lower-quality image than CCDs. We describe an image enhancement algorithm, which includes color interpolation, color correction, gamma correction, sharpening, and automatic exposure control, to compensate for this disadvantage, and present its efficient hardware architecture to implement on the real-time processor. The presented real-time preprocessor was designed using VHDL, and it contains about 19.2K logic gates. We also implement our system on FPGA chips in order to provide the real-time adjustment and it was successfully tested.